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Abstract-Caching popular content at base stations is a powerful supplement to existing limited backhaul links for accommodating the exponentially increasing mobile data traffic. Given the limited cache budget, we investigate the cache size allocation problem in cellular networks to maximize the user success probability (USP), taking wireless channel statistics, backhaul capacities and file popularity distributions into consideration. The USP is defined as the probability that one user can successfully download its requested file either from the local cache or via the backhaul link. We first consider a single-cell scenario and derive a closedform expression for the USP, which helps reveal the impacts of various parameters, such as the file popularity distribution. More specifically, for a highly concentrated file popularity distribution, the required cache size is independent of the total number of files, while for a less concentrated file popularity distribution, the required cache size is in linear relation to the total number of files. Furthermore, we study the multi-cell scenario, and provide a bisection search algorithm to find the optimal cache size allocation. The optimal cache size allocation is verified by simulations, and it is shown to play a more significant role when the file popularity distribution is less concentrated.

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This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs).We show that the relaying matrices have unit rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.Communications over wireless channels continue to be major challenges of today's technologies mainly due to spectrum scarcity and channel fading characteristics. While spectrum utilization depends very much on place and time, it has been well known that most spectrum is heavily under-utilized [1]. Cognitive radio system (CRS) [2] is a new paradigm to improve the spectrum efficiency by allowing a secondary system to access the spectrum licensed to the primary system. In a typical setup, the primary users (PUs) have the priority to access the spectrum, while CRS can occupy the spectrum only if it does not interrupt the communication of the primary system. In practice, this either requires the CRS to sense and detect the spectrum holes and then access the spectrum opportunistically or demands the interference from CRS to the primary system to be properly controlled. In either case, the primary system and CRS are working separately. A major challenge therefore arises to guarantee the quality-of-service (QoS) of cognitive users (CUs) without degrading the primary system performance. In light of this, a number of beamforming techniques have been proposed to achieve various related objectives assuming perfect [3], partial [4] or imperfect channel state information (CSI) [5,6] available at the CRS regarding the primary system.On the other hand, cooperative communication, especially via relaying, is a promising countermeasure for channel fading. Relaying strategies may be categorized into three main types: 1) compress-and-forward (CF) 2) amplify-and-forward (AF) and 3) decode-and-forward (DF). Among them, AF, in which the relay simply performs linear processing on the received noisy signal from the sender and forwards it to the destination, is arguably the most attractive strategy, due to its relatively low implementation complexity.Interested...

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